Influence of Monomer Systems on the Bond Strength Between Resin Composites and Polymerized Fiber-Reinforced Composite upon Aging
Bibliographic record
Abstract
Purpose: This study examined the influence of different monomer systems on the tensile bond strength between a resin composite and a polymerized fiber-reinforced composite (FRC). The influence of the age (shelf-life) of the FRC prepreg (reinforcing fiber pre-impregnated with a resin system) before preparing the FRC substrate for the bonding test was also assessed.Materials and Methods: Semi-interpenetrating polymer network (semi-IPN)-based glass FRC prepregs were aged for various durations (1, 1.5, and 3 years) at 4 degrees C before being used to prepare FRC substrates via light polymerization. Four groups of aged prepregs were prepared through different treatments with: 1. no primer; 2. a dimethacrylate-based adhesive primer; 3. a universal primer; and 4. a specific composite primer. Subsequently, a resin composite luting cement was applied on the treated FRC substrates and cured with light. The water sorption of the FRC-composite specimens was determined. Then, the differences in the tensile bond strength were evaluated using ANOVA (p <= 0.05).Results: There were significant differences in the tensile bond strength between the composite cement and the FRC according to the primer used (p < 0.001), aging time (p < 0.001), and their interactive effect (p < 0.001).Conclusion: The monomers of the universal primer demonstrated the best ability to diffuse into the semi-IPN structure of the polymer matrix of FRC. This improved the interfacial bond strength between the composite cement and the FRC substrate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".